How to Control LLM Costs and Usage with an AI Gateway

Few line items are as jumpy as LLM spend. A provider tweaks its pricing. An agent gets stuck in a loop. A prompt quietly balloons with retrieved context. A feature ships to every user at once. Any of these can double the bill, and the worst part is you usually find out at the end of the month, long after the damage is done.

The root cause is almost always the same: by default, LLM usage is opaque. Each application calls a provider directly, with its own key, and nobody has a live, consolidated view of who’s spending what. An AI gateway fixes that at the source. Once every model call passes through one layer, cost finally becomes something you can measure, attribute, and cap. Let’s go through exactly where the money leaks and how a gateway closes each gap.

Why LLM costs run away

LLM spend doesn’t behave like traditional cloud cost, and three properties make it dangerous:

• It’s metered per token, not per request. One call can cost a fraction of a cent or several dollars depending on context length and output, so request volume tells you very little.
• It’s trivial to multiply. A retry bug, a looping agent, or an unbounded batch job can burn millions of tokens before anyone notices.
• It’s unattributed. Share provider keys across teams and finance gets one invoice with no breakdown by team, app, or feature.

Put those together and the first sign of trouble is usually the bill itself — which is the worst possible time to learn about it.

How an AI gateway controls cost

Real-time tracking and attribution

Visibility comes first. A gateway logs every request with its token counts and computed cost, then ties it back to the user, team, app, or customer that made it. Instead of one opaque number, you get a live breakdown by whatever dimension you care about. TrueFoundry surfaces this through dashboards and OpenTelemetry-based metrics, so the cost data lands in the same observability stack you already watch. Attribution sounds boring, but it’s the thing that turns “AI is expensive” into “this team’s batch job is expensive,” which is a problem you can actually fix.

Budgets that enforce hard limits

Tracking alone won’t stop overspend — enforcement does. A gateway lets you set budget limits per user, team, or app and refuses requests once a limit is hit. That flips cost from something you discover afterward to something you cap in advance. A bug or a runaway agent can’t drain the whole AI budget, because the gateway stops the calls at the threshold you set.

Rate limiting to contain the chaos

Rate limits are the safety valve. Cap requests or tokens per developer, team, or customer tier, and you head off the most common disasters: infinite loops, accidental load tests against prod, and abusive traffic. They’re also handy beyond cost — you can protect a self-hosted model by capping it and bursting to a paid API only when your on-prem GPUs are saturated, or model different customer tiers directly in config.

Semantic caching so you don’t pay twice

A surprising share of production prompts repeat — same questions, same retrieved context, same templates. Semantic caching lets the gateway return a stored answer for a sufficiently similar prompt instead of paying for another model call. On high-traffic, repetitive workloads, that trims both cost and latency without touching application code.

Routing to the right-priced model

Not every request deserves your most expensive model. A gateway can route by cost, latency, or quality — sending easy tasks to cheaper or self-hosted models and saving the premium ones for the hard problems. Balancing across providers also lets you take advantage of price differences and committed-use discounts, and fall back when one provider throttles. Because the API is unified and OpenAI-compatible, this is a config decision, not an application rewrite.

A practical cost-control workflow

Strung together, a typical rollout looks like this:

1. Route all LLM traffic through the gateway so every call is measured in one place.
2. Turn on cost tracking and break spend down by team, app, and model to find the heavy hitters.
3. Set budgets off that baseline, with hard caps for safety.
4. Apply rate limits to contain loops and abuse.
5. Enable semantic caching on repetitive, high-volume endpoints.
6. Add routing rules that match task difficulty to model cost.
7. Review the dashboards regularly and tighten as usage settles.

Each step compounds. Visibility informs budgets, budgets and rate limits prevent disasters, and caching plus routing structurally lower the cost of every call.

Best practices

• Attribute everything from day one. You can’t optimize what you can’t see; tag cost by team, app, and environment.
• Set budgets before you scale, not after the first scary bill.
• Cap non-production environments hard — they rarely need premium models.
• Cache the repetitive stuff like FAQ bots and templated generation.
• Default to the cheapest model that clears your quality bar, and escalate only when you have to.
• Alert on anomalies so a spike triggers action in minutes, not at month-end.

How TrueFoundry helps

TrueFoundry’s AI Gateway brings these controls together in one place. It tracks token cost in real time and attributes it per user, team, and app; enforces budgets and per-user, per-model, and per-application rate limits; and supports semantic caching and cost-aware load balancing across 1,000+ LLMs behind a single OpenAI-compatible API — all at roughly 3 ms of overhead. Because it can run inside your own VPC, sensitive prompts and cost data stay in your environment while you get spend under control.

FAQ

Q: How do you control LLM costs with an AI gateway? A: Route every model call through the gateway and lean on its cost tracking, per-team budgets, rate limits, semantic caching, and cost-aware routing. Together they give you real-time visibility, hard spending caps, and a structurally lower cost per call.

Q: Can an AI gateway stop a runaway agent from overspending? A: Yes. Budget and rate limits cap spend per user, team, or app, so a looping agent or buggy retry gets blocked at its threshold instead of draining the budget.

Q: Does an AI gateway add much latency or cost of its own? A: A well-built one adds very little — TrueFoundry’s adds roughly 3 ms and sustains 350+ RPS on a single vCPU — typically far less than it saves through caching and smarter routing.

Related reading

• LLM Cost Tracking Solution
• What is an LLM Gateway?
• Why Organizations Need an AI Gateway

Conclusion

LLM costs feel unpredictable mostly because teams have no central place to measure and cap them. An AI gateway makes spend visible, attributable, and enforceable — and lowers it structurally through caching and routing. If runaway spend is on your radar, it’s worth seeing how TrueFoundry’s AI Gateway puts the numbers back in your control.